v12-dfs

betting_best_bets

Best Bets

THE board sweep — scans EVERY main prop market (HR/RBI/hit/total_bases/ 2+hits/stolen_base) in one call and returns the top edges across all of them, not just home runs. Power props carry the Savant validation (CONFIRMED power ranked above neutral/noise). Use this for an open 'what's hot / best plays / what do you recommend tonight' so you NEVER conclude off a single-market scan. A SCREEN vs the vig line — validate with CLV; game-side value is separate (betting_sharp / betting_game_model).

Responses:

200: Successful Response (Success Response) Content-Type: application/json 422: Validation Error Content-Type: application/json

Example Response:

{
  "detail": [
    {
      "loc": [],
      "msg": "Message",
      "type": "Error Type",
      "ctx": {}
    }
  ]
}

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`min_edge`	No

TDQS

A3.6/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, description provides some behavioral context (e.g., scans all markets, power props have Savant validation, screen vs vig line) but lacks details on destructive potential, authentication, or rate limits. Adequate but not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is somewhat verbose with boilerplate response codes and an example that is not tool-specific. Main purpose is front-loaded but includes unnecessary formatting. Could be more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 2 default parameters and no output schema, the description explains the tool's scope and alternative tools well, but missing parameter documentation and output expectations leaves gaps for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, yet the description does not explain the 'limit' and 'min_edge' parameters or how they affect results. Completely fails to compensate for schema's lack of param info.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool scans all main prop markets in one call and returns top edges, distinguishing it from single-market scans and mentioning sibling tools (betting_sharp, betting_game_model) for separate use cases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance: 'Use this for an open what's hot / best plays / what do you recommend tonight' and explicitly excludes game-side value, directing to siblings. Clear when to use and when not to.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.8/5.0

Disambiguation3/5

The betting_* cluster is clearly namespaced, but betting_best_bets and betting_scan_edges both return a ranked board of top prop edges, and betting_market overlaps with betting_game_lines and betting_sharp/cross_book in purpose. The detailed descriptions reduce misselection, but several boundaries are not crisp.

Naming Consistency3/5

The set is uniformly snake_case with helpful cluster prefixes like betting_, list_, and slate_, so it reads predictably. However, it mixes verb_noun names (generate_lineups, run_mlb_postmortem), noun phrases (betting_market, health_check/health_v12), and adjective-noun names (betting_best_bets, betting_sharp), so there isn't one consistent pattern.

Tool Count2/5

34 tools is above the 25+ threshold for a single MCP server, even considering the combined betting/DFS/contest scope. Several tools could be consolidated — betting_best_bets vs betting_scan_edges, health_check vs health_v12, and the two guides — making the surface feel heavy rather than lean.

Completeness5/5

The betting lifecycle is covered end-to-end: raw markets, models, edge scans, value checks, parlay building, bet logging/settlement, and P&L/CLV. The DFS side covers slates, player pools, lineup generation/fill, presets, contests, diff/health/refresh, and postmortems, leaving no obvious dead end for agents.

Resources

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